OBJECTIVE: This study was an attempt to identify key CT features that can potentially be used to differentiate between lipid-poor renal angiomyolipoma and renal cell carcinoma (RCC). MATERIALS AND METHODS: We conducted an analysis of patients who received nephrectomy or renal biopsy from 2002 to 2011 with suspected RCC. We included tumors smaller than 7 cm with a completed three-phase CT examination. A radiologist and a urology fellow, blinded to histopathologic diagnosis, recorded the imaging findings by consensus and compared the values for each parameter between lipid-poor angiomyolipoma, RCC subtypes, and RCC as a group. Multivariate logistic regression analysis was performed for each univariate significant feature. RESULTS: The sample in our study consisted of 132 patients with 135 renal tumors, including 51 men (age range, 26-84 years; mean age, 57 years) and 81 women (age range, 29-91 years; mean age, 57 years). These tumors included 33 lipid-poor angiomyolipomas, 54 clear-cell RCC, 31 chromophobe RCC, and 17 papillary RCC. Multivariate analysis revealed four significant parameters for differentiating RCC as a group from lipid-poor angiomyolipoma (angular interface, p = 0.023; hypodense rim, p = 0.045; homogeneity, p = 0.005; unenhanced attenuation > 38.5 HU, p < 0.001), five for clear-cell RCC, two for chromophobe RCC, and one for papillary RCC. Lipid-poor angiomyolipoma and clear-cell RCC showed early strong enhancement and a washout pattern, whereas chromophobe RCC and papillary RCC showed gradual enhancement over time. CONCLUSION: Specific CT features can potentially be used to differentiate lipid-poor renal angiomyolipoma from renal cell carcinoma.
OBJECTIVE: This study was an attempt to identify key CT features that can potentially be used to differentiate between lipid-poor renal angiomyolipoma and renal cell carcinoma (RCC). MATERIALS AND METHODS: We conducted an analysis of patients who received nephrectomy or renal biopsy from 2002 to 2011 with suspected RCC. We included tumors smaller than 7 cm with a completed three-phase CT examination. A radiologist and a urology fellow, blinded to histopathologic diagnosis, recorded the imaging findings by consensus and compared the values for each parameter between lipid-poor angiomyolipoma, RCC subtypes, and RCC as a group. Multivariate logistic regression analysis was performed for each univariate significant feature. RESULTS: The sample in our study consisted of 132 patients with 135 renal tumors, including 51 men (age range, 26-84 years; mean age, 57 years) and 81 women (age range, 29-91 years; mean age, 57 years). These tumors included 33 lipid-poor angiomyolipomas, 54 clear-cell RCC, 31 chromophobe RCC, and 17 papillary RCC. Multivariate analysis revealed four significant parameters for differentiating RCC as a group from lipid-poor angiomyolipoma (angular interface, p = 0.023; hypodense rim, p = 0.045; homogeneity, p = 0.005; unenhanced attenuation > 38.5 HU, p < 0.001), five for clear-cell RCC, two for chromophobe RCC, and one for papillary RCC. Lipid-poor angiomyolipoma and clear-cell RCC showed early strong enhancement and a washout pattern, whereas chromophobe RCC and papillary RCC showed gradual enhancement over time. CONCLUSION: Specific CT features can potentially be used to differentiate lipid-poor renal angiomyolipoma from renal cell carcinoma.
Authors: Nicola Schieda; Marc Dilauro; Bardia Moosavi; Taryn Hodgdon; Gregory O Cron; Matthew D F McInnes; Trevor A Flood Journal: Eur Radiol Date: 2015-10-20 Impact factor: 5.315
Authors: Bino A Varghese; Frank Chen; Darryl H Hwang; Steven Y Cen; Inderbir S Gill; Vinay A Duddalwar Journal: Br J Radiol Date: 2018-06-21 Impact factor: 3.039
Authors: Shaheed W Hakim; Nicola Schieda; Taryn Hodgdon; Matthew D F McInnes; Marc Dilauro; Trevor A Flood Journal: Eur Radiol Date: 2015-06-03 Impact factor: 5.315
Authors: Massimo Galia; Domenico Albano; Alberto Bruno; Antonino Agrusa; Giorgio Romano; Giuseppe Di Buono; Francesco Agnello; Giuseppe Salvaggio; Ludovico La Grutta; Massimo Midiri; Roberto Lagalla Journal: Br J Radiol Date: 2017-07-13 Impact factor: 3.039
Authors: Kathleen Nguyen; Nicola Schieda; Nick James; Matthew D F McInnes; Mark Wu; Rebecca E Thornhill Journal: Eur Radiol Date: 2020-09-10 Impact factor: 5.315